Overview

Dataset statistics

Number of variables13
Number of observations731
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.4 KiB
Average record size in memory104.2 B

Variable types

Numeric8
Categorical5

Alerts

mnth is highly overall correlated with seasonHigh correlation
weekday is highly overall correlated with workingdayHigh correlation
temp is highly overall correlated with atemp and 2 other fieldsHigh correlation
atemp is highly overall correlated with temp and 2 other fieldsHigh correlation
hum is highly overall correlated with weathersitHigh correlation
rentals is highly overall correlated with temp and 2 other fieldsHigh correlation
season is highly overall correlated with mnth and 2 other fieldsHigh correlation
workingday is highly overall correlated with weekday and 1 other fieldsHigh correlation
weathersit is highly overall correlated with humHigh correlation
holiday is highly imbalanced (81.2%)Imbalance
weekday has 105 (14.4%) zerosZeros

Reproduction

Analysis started2023-08-19 20:47:23.131319
Analysis finished2023-08-19 20:47:31.416151
Duration8.28 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

day
Real number (ℝ)

Distinct31
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.738714
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:31.530382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8099488
Coefficient of variation (CV)0.55976294
Kurtosis-1.1948637
Mean15.738714
Median Absolute Deviation (MAD)8
Skewness0.0060078758
Sum11505
Variance77.615198
MonotonicityNot monotonic
2023-08-19T17:47:31.661503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 24
 
3.3%
2 24
 
3.3%
28 24
 
3.3%
27 24
 
3.3%
26 24
 
3.3%
25 24
 
3.3%
24 24
 
3.3%
23 24
 
3.3%
22 24
 
3.3%
21 24
 
3.3%
Other values (21) 491
67.2%
ValueCountFrequency (%)
1 24
3.3%
2 24
3.3%
3 24
3.3%
4 24
3.3%
5 24
3.3%
6 24
3.3%
7 24
3.3%
8 24
3.3%
9 24
3.3%
10 24
3.3%
ValueCountFrequency (%)
31 14
1.9%
30 22
3.0%
29 23
3.1%
28 24
3.3%
27 24
3.3%
26 24
3.3%
25 24
3.3%
24 24
3.3%
23 24
3.3%
22 24
3.3%

mnth
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5198358
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:31.803482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4519128
Coefficient of variation (CV)0.52944781
Kurtosis-1.209112
Mean6.5198358
Median Absolute Deviation (MAD)3
Skewness-0.0081486501
Sum4766
Variance11.915702
MonotonicityNot monotonic
2023-08-19T17:47:31.935797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 62
8.5%
3 62
8.5%
5 62
8.5%
7 62
8.5%
8 62
8.5%
10 62
8.5%
12 62
8.5%
4 60
8.2%
6 60
8.2%
9 60
8.2%
Other values (2) 117
16.0%
ValueCountFrequency (%)
1 62
8.5%
2 57
7.8%
3 62
8.5%
4 60
8.2%
5 62
8.5%
6 60
8.2%
7 62
8.5%
8 62
8.5%
9 60
8.2%
10 62
8.5%
ValueCountFrequency (%)
12 62
8.5%
11 60
8.2%
10 62
8.5%
9 60
8.2%
8 62
8.5%
7 62
8.5%
6 60
8.2%
5 62
8.5%
4 60
8.2%
3 62
8.5%

year
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2012
366 
2011
365 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2924
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2012 366
50.1%
2011 365
49.9%

Length

2023-08-19T17:47:32.076280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T17:47:32.194364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 366
50.1%
2011 365
49.9%

Most occurring characters

ValueCountFrequency (%)
2 1097
37.5%
1 1096
37.5%
0 731
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2924
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1097
37.5%
1 1096
37.5%
0 731
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1097
37.5%
1 1096
37.5%
0 731
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1097
37.5%
1 1096
37.5%
0 731
25.0%

season
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3
188 
2
184 
1
181 
4
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Length

2023-08-19T17:47:32.306168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T17:47:32.409840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring characters

ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 188
25.7%
2 184
25.2%
1 181
24.8%
4 178
24.4%

holiday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
0
710 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Length

2023-08-19T17:47:32.544153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T17:47:32.643702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 710
97.1%
1 21
 
2.9%

weekday
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997264
Minimum0
Maximum6
Zeros105
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:32.730114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0047869
Coefficient of variation (CV)0.66887231
Kurtosis-1.2542824
Mean2.997264
Median Absolute Deviation (MAD)2
Skewness0.0027415977
Sum2191
Variance4.0191706
MonotonicityNot monotonic
2023-08-19T17:47:32.836024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 105
14.4%
0 105
14.4%
1 105
14.4%
2 104
14.2%
3 104
14.2%
4 104
14.2%
5 104
14.2%
ValueCountFrequency (%)
0 105
14.4%
1 105
14.4%
2 104
14.2%
3 104
14.2%
4 104
14.2%
5 104
14.2%
6 105
14.4%
ValueCountFrequency (%)
6 105
14.4%
5 104
14.2%
4 104
14.2%
3 104
14.2%
2 104
14.2%
1 105
14.4%
0 105
14.4%

workingday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
500 
0
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Length

2023-08-19T17:47:32.952899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T17:47:33.050542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring characters

ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common 731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 500
68.4%
0 231
31.6%

weathersit
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
463 
2
247 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Length

2023-08-19T17:47:33.155518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T17:47:33.256906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 731
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 731
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 463
63.3%
2 247
33.8%
3 21
 
2.9%

temp
Real number (ℝ)

Distinct499
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49538479
Minimum0.0591304
Maximum0.861667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:33.382467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0591304
5-th percentile0.2135685
Q10.3370835
median0.498333
Q30.6554165
95-th percentile0.76875
Maximum0.861667
Range0.8025366
Interquartile range (IQR)0.318333

Descriptive statistics

Standard deviation0.183051
Coefficient of variation (CV)0.36951275
Kurtosis-1.1188642
Mean0.49538479
Median Absolute Deviation (MAD)0.158333
Skewness-0.054520965
Sum362.12628
Variance0.033507667
MonotonicityNot monotonic
2023-08-19T17:47:33.554045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.635 5
 
0.7%
0.265833 5
 
0.7%
0.68 4
 
0.5%
0.710833 4
 
0.5%
0.564167 4
 
0.5%
0.484167 4
 
0.5%
0.649167 4
 
0.5%
0.696667 4
 
0.5%
0.4375 4
 
0.5%
0.606667 3
 
0.4%
Other values (489) 690
94.4%
ValueCountFrequency (%)
0.0591304 1
0.1%
0.0965217 1
0.1%
0.0973913 1
0.1%
0.1075 1
0.1%
0.1275 1
0.1%
0.134783 1
0.1%
0.138333 1
0.1%
0.144348 1
0.1%
0.15 1
0.1%
0.150833 1
0.1%
ValueCountFrequency (%)
0.861667 1
0.1%
0.849167 1
0.1%
0.848333 1
0.1%
0.838333 1
0.1%
0.834167 1
0.1%
0.83 1
0.1%
0.828333 1
0.1%
0.8275 1
0.1%
0.8225 1
0.1%
0.818333 1
0.1%

atemp
Real number (ℝ)

Distinct690
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47435399
Minimum0.0790696
Maximum0.840896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:33.730044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0790696
5-th percentile0.2206455
Q10.3378425
median0.486733
Q30.608602
95-th percentile0.714967
Maximum0.840896
Range0.7618264
Interquartile range (IQR)0.2707595

Descriptive statistics

Standard deviation0.16296118
Coefficient of variation (CV)0.34354339
Kurtosis-0.98513053
Mean0.47435399
Median Absolute Deviation (MAD)0.135624
Skewness-0.13108804
Sum346.75277
Variance0.026556346
MonotonicityNot monotonic
2023-08-19T17:47:34.091461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.654688 4
 
0.5%
0.375621 3
 
0.4%
0.637008 3
 
0.4%
0.571975 2
 
0.3%
0.466525 2
 
0.3%
0.607962 2
 
0.3%
0.654042 2
 
0.3%
0.32575 2
 
0.3%
0.595346 2
 
0.3%
0.39835 2
 
0.3%
Other values (680) 707
96.7%
ValueCountFrequency (%)
0.0790696 1
0.1%
0.0988391 1
0.1%
0.101658 1
0.1%
0.116175 1
0.1%
0.11793 1
0.1%
0.119337 1
0.1%
0.126275 1
0.1%
0.144283 1
0.1%
0.149548 1
0.1%
0.150883 1
0.1%
ValueCountFrequency (%)
0.840896 1
0.1%
0.826371 1
0.1%
0.804913 1
0.1%
0.804287 1
0.1%
0.794829 1
0.1%
0.790396 1
0.1%
0.786613 1
0.1%
0.785967 1
0.1%
0.761367 1
0.1%
0.757579 1
0.1%

hum
Real number (ℝ)

Distinct595
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62789406
Minimum0
Maximum0.9725
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:34.242184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4074545
Q10.52
median0.626667
Q30.7302085
95-th percentile0.8685415
Maximum0.9725
Range0.9725
Interquartile range (IQR)0.2102085

Descriptive statistics

Standard deviation0.1424291
Coefficient of variation (CV)0.22683619
Kurtosis-0.064530135
Mean0.62789406
Median Absolute Deviation (MAD)0.104584
Skewness-0.069783434
Sum458.99056
Variance0.020286047
MonotonicityNot monotonic
2023-08-19T17:47:34.389229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.613333 4
 
0.5%
0.605 3
 
0.4%
0.59 3
 
0.4%
0.538333 3
 
0.4%
0.69 3
 
0.4%
0.57 3
 
0.4%
0.568333 3
 
0.4%
0.722917 3
 
0.4%
0.552083 3
 
0.4%
0.74125 3
 
0.4%
Other values (585) 700
95.8%
ValueCountFrequency (%)
0 1
0.1%
0.187917 1
0.1%
0.254167 1
0.1%
0.275833 1
0.1%
0.29 1
0.1%
0.302174 1
0.1%
0.305 1
0.1%
0.31125 1
0.1%
0.314167 1
0.1%
0.314348 1
0.1%
ValueCountFrequency (%)
0.9725 1
0.1%
0.970417 1
0.1%
0.9625 1
0.1%
0.949583 1
0.1%
0.948261 1
0.1%
0.939565 1
0.1%
0.93 1
0.1%
0.929167 1
0.1%
0.925 1
0.1%
0.9225 1
0.1%

windspeed
Real number (ℝ)

Distinct650
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19048621
Minimum0.0223917
Maximum0.507463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:34.526438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0223917
5-th percentile0.07961665
Q10.13495
median0.180975
Q30.2332145
95-th percentile0.343283
Maximum0.507463
Range0.4850713
Interquartile range (IQR)0.0982645

Descriptive statistics

Standard deviation0.077497871
Coefficient of variation (CV)0.40684242
Kurtosis0.41092227
Mean0.19048621
Median Absolute Deviation (MAD)0.049129
Skewness0.67734542
Sum139.24542
Variance0.00600592
MonotonicityNot monotonic
2023-08-19T17:47:34.674829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.134954 3
 
0.4%
0.228858 3
 
0.4%
0.136817 3
 
0.4%
0.1107 3
 
0.4%
0.118792 3
 
0.4%
0.149883 3
 
0.4%
0.167912 3
 
0.4%
0.166667 3
 
0.4%
0.10635 3
 
0.4%
0.180975 2
 
0.3%
Other values (640) 702
96.0%
ValueCountFrequency (%)
0.0223917 1
0.1%
0.0423042 1
0.1%
0.0454042 1
0.1%
0.0454083 1
0.1%
0.04665 1
0.1%
0.047275 1
0.1%
0.0503792 1
0.1%
0.0528708 1
0.1%
0.053213 1
0.1%
0.057225 1
0.1%
ValueCountFrequency (%)
0.507463 1
0.1%
0.441563 1
0.1%
0.422275 1
0.1%
0.421642 1
0.1%
0.417908 1
0.1%
0.415429 1
0.1%
0.4148 1
0.1%
0.409212 1
0.1%
0.407346 1
0.1%
0.398008 1
0.1%

rentals
Real number (ℝ)

Distinct606
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.17647
Minimum2
Maximum3410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-08-19T17:47:34.807164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile88
Q1315.5
median713
Q31096
95-th percentile2355
Maximum3410
Range3408
Interquartile range (IQR)780.5

Descriptive statistics

Standard deviation686.62249
Coefficient of variation (CV)0.80952787
Kurtosis1.3220743
Mean848.17647
Median Absolute Deviation (MAD)396
Skewness1.266454
Sum620017
Variance471450.44
MonotonicityNot monotonic
2023-08-19T17:47:34.944823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 4
 
0.5%
968 4
 
0.5%
163 3
 
0.4%
653 3
 
0.4%
123 3
 
0.4%
140 3
 
0.4%
244 3
 
0.4%
639 3
 
0.4%
775 3
 
0.4%
1198 2
 
0.3%
Other values (596) 700
95.8%
ValueCountFrequency (%)
2 1
0.1%
9 2
0.3%
15 1
0.1%
25 1
0.1%
34 1
0.1%
38 2
0.3%
41 1
0.1%
42 1
0.1%
43 1
0.1%
46 1
0.1%
ValueCountFrequency (%)
3410 1
0.1%
3283 1
0.1%
3252 1
0.1%
3160 1
0.1%
3155 1
0.1%
3065 1
0.1%
3031 1
0.1%
2963 1
0.1%
2855 1
0.1%
2846 1
0.1%

Interactions

2023-08-19T17:47:30.280305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:23.820962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.868487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.909547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.842604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.700243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.592995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.367806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.379876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:23.924535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.044279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.123794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.943862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.828441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.690499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.569036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.483742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.019954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.171934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.239300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.042741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.948577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.781371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.663078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.581127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.116381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.290080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.344227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.145321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.064020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.874485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.762926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.692171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.230573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.416089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.451725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.254400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.183065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.979060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.890310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.800898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.331148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.541097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.551725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.376120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.287418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.085940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.992711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.897894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.429572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.656142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.645529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.475169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.393606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.175484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.088855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.995481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:24.667013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:25.790401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:26.742863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:27.584001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:28.490579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:29.268588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T17:47:30.181217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-19T17:47:35.047432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
daymnthweekdaytempatemphumwindspeedrentalsyearseasonholidayworkingdayweathersit
day1.0000.009-0.0000.0200.0140.0400.019-0.0060.0000.0000.0000.0000.045
mnth0.0091.0000.0090.2080.2090.213-0.2070.1850.0000.8840.0000.0000.104
weekday-0.0000.0091.000-0.004-0.013-0.0540.0130.0400.0000.0000.2680.9350.040
temp0.0200.208-0.0041.0000.9930.130-0.1470.6670.1120.5720.0000.0410.148
atemp0.0140.209-0.0130.9931.0000.140-0.1690.6680.0820.5770.0220.0880.170
hum0.0400.213-0.0540.1300.1401.000-0.239-0.0710.1520.1510.0000.0550.551
windspeed0.019-0.2070.013-0.147-0.169-0.2391.000-0.1800.0620.1900.0000.0450.110
rentals-0.0060.1850.0400.6670.668-0.071-0.1801.0000.3170.3740.0360.5710.221
year0.0000.0000.0000.1120.0820.1520.0620.3171.0000.0000.0000.0000.054
season0.0000.8840.0000.5720.5770.1510.1900.3740.0001.0000.0000.0000.078
holiday0.0000.0000.2680.0000.0220.0000.0000.0360.0000.0001.0000.2420.000
workingday0.0000.0000.9350.0410.0880.0550.0450.5710.0000.0000.2421.0000.032
weathersit0.0450.1040.0400.1480.1700.5510.1100.2210.0540.0780.0000.0321.000

Missing values

2023-08-19T17:47:31.140385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-19T17:47:31.336096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

daymnthyearseasonholidayweekdayworkingdayweathersittempatemphumwindspeedrentals
0112011106020.3441670.3636250.8058330.160446331
1212011100020.3634780.3537390.6960870.248539131
2312011101110.1963640.1894050.4372730.248309120
3412011102110.2000000.2121220.5904350.160296108
4512011103110.2269570.2292700.4369570.18690082
5612011104110.2043480.2332090.5182610.08956588
6712011105120.1965220.2088390.4986960.168726148
7812011106020.1650000.1622540.5358330.26680468
8912011100010.1383330.1161750.4341670.36195054
91012011101110.1508330.1508880.4829170.22326741
daymnthyearseasonholidayweekdayworkingdayweathersittempatemphumwindspeedrentals
72122122012106010.2658330.2361130.4412500.407346205
72223122012100010.2458330.2594710.5154170.133083408
72324122012101120.2313040.2589000.7913040.077230174
72425122012112020.2913040.2944650.7347830.168726440
72526122012103130.2433330.2203330.8233330.3165469
72627122012104120.2541670.2266420.6529170.350133247
72728122012105120.2533330.2550460.5900000.155471644
72829122012106020.2533330.2424000.7529170.124383159
72930122012100010.2558330.2317000.4833330.350754364
73031122012101120.2158330.2234870.5775000.154846439